
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


lowercase    :   List[Any]						=			False




class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:int		)						->					Dict:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	@property
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Any:
								return 1_2







	@property
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Dict:
								return 1_2







	@property
	def   _lowerCamelCase							(   self						:Optional[int]		)						->					str:
								return 3_2







	@property
	def   _lowerCamelCase							(   self						:Tuple		)						->					List[str]:
								torch.manual_seed(0		)
								__UpperCamelCase      :  Optional[int]       =   VQModel(
								    block_out_channels=[3_2, 6_4]	,  in_channels=3	,  out_channels=3	,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,  latent_channels=3	,  num_vq_embeddings=self.num_embed	,  vq_embed_dim=3	,  )
								return model







	@property
	def   _lowerCamelCase							(   self						:int		)						->					int:
								__UpperCamelCase      :  int       =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
								return tokenizer







	@property
	def   _lowerCamelCase							(   self						:Any		)						->					List[Any]:
								torch.manual_seed(0		)
								__UpperCamelCase      :  int       =   CLIPTextConfig(
								    bos_token_id=0	,  eos_token_id=2	,  hidden_size=self.text_embedder_hidden_size	,  intermediate_size=3_7	,  layer_norm_eps=1E-0_5	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_0_0_0	,  )
								return CLIPTextModel(SCREAMING_SNAKE_CASE_		)







	@property
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Union[str, Any]:
								torch.manual_seed(0		)

								__UpperCamelCase      :  List[str]       =   1_2
								__UpperCamelCase      :  Union[str, Any]       =   1_2

								__UpperCamelCase      :  Union[str, Any]       =   {
								    "attention_bias": True,
								    "cross_attention_dim": 3_2,
								    "attention_head_dim": height * width,
								    "num_attention_heads": 1,
								    "num_vector_embeds": self.num_embed,
								    "num_embeds_ada_norm": self.num_embeds_ada_norm,
								    "norm_num_groups": 3_2,
								    "sample_size": width,
								    "activation_fn": "geglu-approximate",
								}

								__UpperCamelCase      :  Any       =   TransformeraDModel(**SCREAMING_SNAKE_CASE_		)
								return model







	def   _lowerCamelCase							(   self						:Any		)						->					Union[str, Any]:
								__UpperCamelCase      :  Optional[Any]       =   "cpu"

								__UpperCamelCase      :  Dict       =   self.dummy_vqvae
								__UpperCamelCase      :  List[str]       =   self.dummy_text_encoder
								__UpperCamelCase      :  int       =   self.dummy_tokenizer
								__UpperCamelCase      :  Union[str, Any]       =   self.dummy_transformer
								__UpperCamelCase      :  Optional[Any]       =   VQDiffusionScheduler(self.num_embed		)
								__UpperCamelCase      :  List[Any]       =   LearnedClassifierFreeSamplingEmbeddings(learnable=SCREAMING_SNAKE_CASE_		)

								__UpperCamelCase      :  List[str]       =   VQDiffusionPipeline(
								    vqvae=SCREAMING_SNAKE_CASE_	,  text_encoder=SCREAMING_SNAKE_CASE_	,  tokenizer=SCREAMING_SNAKE_CASE_	,  transformer=SCREAMING_SNAKE_CASE_	,  scheduler=SCREAMING_SNAKE_CASE_	,  learned_classifier_free_sampling_embeddings=SCREAMING_SNAKE_CASE_	,  )
								__UpperCamelCase      :  List[Any]       =   pipe.to(SCREAMING_SNAKE_CASE_		)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_		)

								__UpperCamelCase      :  int       =   "teddy bear playing in the pool"

								__UpperCamelCase      :  int       =   torch.Generator(device=SCREAMING_SNAKE_CASE_		).manual_seed(0		)
								__UpperCamelCase      :  List[str]       =   pipe([prompt]	,  generator=SCREAMING_SNAKE_CASE_	,  num_inference_steps=2	,  output_type="np"		)
								__UpperCamelCase      :  str       =   output.images

								__UpperCamelCase      :  str       =   torch.Generator(device=SCREAMING_SNAKE_CASE_		).manual_seed(0		)
								__UpperCamelCase      :  Union[str, Any]       =   pipe(
								    [prompt]	,  generator=SCREAMING_SNAKE_CASE_	,  output_type="np"	,  return_dict=SCREAMING_SNAKE_CASE_	,  num_inference_steps=2		)[0]

								__UpperCamelCase      :  Dict       =   image[0, -3:, -3:, -1]
								__UpperCamelCase      :  Optional[int]       =   image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 2_4, 2_4, 3)

								__UpperCamelCase      :  Tuple       =   np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2







	def   _lowerCamelCase							(   self						:List[str]		)						->					Dict:
								__UpperCamelCase      :  Optional[int]       =   "cpu"

								__UpperCamelCase      :  Dict       =   self.dummy_vqvae
								__UpperCamelCase      :  Optional[int]       =   self.dummy_text_encoder
								__UpperCamelCase      :  str       =   self.dummy_tokenizer
								__UpperCamelCase      :  Dict       =   self.dummy_transformer
								__UpperCamelCase      :  int       =   VQDiffusionScheduler(self.num_embed		)
								__UpperCamelCase      :  Union[str, Any]       =   LearnedClassifierFreeSamplingEmbeddings(
								    learnable=SCREAMING_SNAKE_CASE_	,  hidden_size=self.text_embedder_hidden_size	,  length=tokenizer.model_max_length		)

								__UpperCamelCase      :  List[Any]       =   VQDiffusionPipeline(
								    vqvae=SCREAMING_SNAKE_CASE_	,  text_encoder=SCREAMING_SNAKE_CASE_	,  tokenizer=SCREAMING_SNAKE_CASE_	,  transformer=SCREAMING_SNAKE_CASE_	,  scheduler=SCREAMING_SNAKE_CASE_	,  learned_classifier_free_sampling_embeddings=SCREAMING_SNAKE_CASE_	,  )
								__UpperCamelCase      :  List[Any]       =   pipe.to(SCREAMING_SNAKE_CASE_		)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_		)

								__UpperCamelCase      :  Union[str, Any]       =   "teddy bear playing in the pool"

								__UpperCamelCase      :  Optional[Any]       =   torch.Generator(device=SCREAMING_SNAKE_CASE_		).manual_seed(0		)
								__UpperCamelCase      :  Union[str, Any]       =   pipe([prompt]	,  generator=SCREAMING_SNAKE_CASE_	,  num_inference_steps=2	,  output_type="np"		)
								__UpperCamelCase      :  List[str]       =   output.images

								__UpperCamelCase      :  Dict       =   torch.Generator(device=SCREAMING_SNAKE_CASE_		).manual_seed(0		)
								__UpperCamelCase      :  List[Any]       =   pipe(
								    [prompt]	,  generator=SCREAMING_SNAKE_CASE_	,  output_type="np"	,  return_dict=SCREAMING_SNAKE_CASE_	,  num_inference_steps=2		)[0]

								__UpperCamelCase      :  Optional[Any]       =   image[0, -3:, -3:, -1]
								__UpperCamelCase      :  Optional[Any]       =   image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 2_4, 2_4, 3)

								__UpperCamelCase      :  Tuple       =   np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2




@slow
@require_torch_gpu
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:List[str]		)						->					List[str]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	def   _lowerCamelCase							(   self						:List[str]		)						->					Dict:
								__UpperCamelCase      :  List[str]       =   load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"		)

								__UpperCamelCase      :  List[str]       =   VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"		)
								__UpperCamelCase      :  Union[str, Any]       =   pipeline.to(SCREAMING_SNAKE_CASE_		)
								pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_		)

								# requires GPU generator for gumbel softmax
								# don't use GPU generator in tests though
								__UpperCamelCase      :  Optional[Any]       =   torch.Generator(device=SCREAMING_SNAKE_CASE_		).manual_seed(0		)
								__UpperCamelCase      :  Any       =   pipeline(
								    "teddy bear playing in the pool"	,  num_images_per_prompt=1	,  generator=SCREAMING_SNAKE_CASE_	,  output_type="np"	,  )

								__UpperCamelCase      :  List[Any]       =   output.images[0]

								assert image.shape == (2_5_6, 2_5_6, 3)
								assert np.abs(expected_image - image		).max() < 2.0

"""simple docstring"""






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch



@require_torch
@require_optimum
@slow
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  tokenizer('''This is me'''      ,return_tensors='''pt'''			)

				lowerCAmelCase												=  model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				lowerCAmelCase												=  model.generate(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

					self.assertFalse(
					    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

					lowerCAmelCase												=  model_reloaded.generate(**SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
						model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  model.reverse_bettertransformer()
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)


